org.apache.spark.ml.classification

Class NaiveBayes

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<M>

org.apache.spark.ml.Predictor<FeaturesType,E,M>

org.apache.spark.ml.classification.Classifier<FeaturesType,E,M>

org.apache.spark.ml.classification.ProbabilisticClassifier<Vector,NaiveBayes,NaiveBayesModel>

org.apache.spark.ml.classification.NaiveBayes

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, ClassifierParams, NaiveBayesParams, ProbabilisticClassifierParams, Params, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasProbabilityCol, HasRawPredictionCol, HasThresholds, HasWeightCol, PredictorParams, DefaultParamsWritable, Identifiable, MLWritable

public class NaiveBayes
extends ProbabilisticClassifier<Vector,NaiveBayes,NaiveBayesModel>
implements NaiveBayesParams, DefaultParamsWritable

Naive Bayes Classifiers. It supports Multinomial NB (see here) which can handle finitely supported discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a binary (0/1) data, it can also be used as Bernoulli NB (see here). The input feature values for Multinomial NB and Bernoulli NB must be nonnegative. Since 3.0.0, it supports Complement NB which is an adaptation of the Multinomial NB. Specifically, Complement NB uses statistics from the complement of each class to compute the model's coefficients The inventors of Complement NB show empirically that the parameter estimates for CNB are more stable than those for Multinomial NB. Like Multinomial NB, the input feature values for Complement NB must be nonnegative. Since 3.0.0, it also supports Gaussian NB (see here) which can handle continuous data.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor and Description
NaiveBayes()
NaiveBayes(String uid)

Method Summary

Modifier and Type	Method and Description
NaiveBayes	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static NaiveBayes	load(String path)
Param<String>	modelType() The model type which is a string (case-sensitive).
static MLReader<T>	read()
NaiveBayes	setModelType(String value) Set the model type using a string (case-sensitive).
NaiveBayes	setSmoothing(double value) Set the smoothing parameter.
NaiveBayes	setWeightCol(String value) Sets the value of param weightCol.
DoubleParam	smoothing() The smoothing parameter.
String	uid() An immutable unique ID for the object and its derivatives.
Param<String>	weightCol() Param for weight column name.

Methods inherited from class org.apache.spark.ml.classification.ProbabilisticClassifier

probabilityCol, setProbabilityCol, setThresholds, thresholds

Methods inherited from class org.apache.spark.ml.classification.Classifier

rawPredictionCol, setRawPredictionCol

Methods inherited from class org.apache.spark.ml.Predictor

featuresCol, fit, labelCol, predictionCol, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.classification.NaiveBayesParams

getModelType, getSmoothing

Methods inherited from interface org.apache.spark.ml.PredictorParams

validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol, labelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

featuresCol, getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol, predictionCol

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol

getWeightCol

Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable

write

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.ml.classification.ProbabilisticClassifierParams

validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasRawPredictionCol

getRawPredictionCol, rawPredictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasProbabilityCol

getProbabilityCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasThresholds

getThresholds

Methods inherited from interface org.apache.spark.internal.Logging

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Constructor Detail

NaiveBayes

public NaiveBayes(String uid)

NaiveBayes

public NaiveBayes()

Method Detail

load

public static NaiveBayes load(String path)

read

public static MLReader<T> read()

smoothing

public final DoubleParam smoothing()

Description copied from interface: NaiveBayesParams

The smoothing parameter. (default = 1.0).

Specified by:

smoothing in interface NaiveBayesParams

Returns:

(undocumented)

modelType

public final Param<String> modelType()

Description copied from interface: NaiveBayesParams

The model type which is a string (case-sensitive). Supported options: "multinomial", "complement", "bernoulli", "gaussian". (default = multinomial)

Specified by:

modelType in interface NaiveBayesParams

Returns:

(undocumented)

weightCol

public final Param<String> weightCol()

Description copied from interface: HasWeightCol

Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

Specified by:

weightCol in interface HasWeightCol

Returns:

(undocumented)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setSmoothing

public NaiveBayes setSmoothing(double value)

Set the smoothing parameter. Default is 1.0.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setModelType

public NaiveBayes setModelType(String value)

Set the model type using a string (case-sensitive). Supported options: "multinomial", "complement", "bernoulli", and "gaussian". Default is "multinomial"

Parameters:

value - (undocumented)

Returns:

(undocumented)

setWeightCol

public NaiveBayes setWeightCol(String value)

Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

Parameters:

value - (undocumented)

Returns:

(undocumented)

copy

public NaiveBayes copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,NaiveBayes,NaiveBayesModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)